Danel Draguljić

Noncollapsing Space-Filling Designs for Bounded Nonrectangular Regions.

Many researchers use computer simulators as experimental tools, especially when physical experiments are infeasible. When computer codes are computationally intensive, nonparametric predictors can be fitted to training data for detailed exploration of the input–output relationship. The accuracy of such flexible predictors is enhanced by taking training inputs to be “space-filling.” If there are inputs that have little or no effect on the response, it is desirable that the design be “noncollapsing” in the sense of having space-filling lower dimensional projections. This article describes an algorithm for constructing noncollapsing space-filling designs for bounded input regions that are of possibly high dimension. Online supplementary materials provide the code for the algorithm, examples of its use, and show its performance in multiple settings.

Automated evolutionary optimization of ion channel conductances and kinetics in models of young and aged rhesus monkey pyramidal neurons

Conductance-based compartment modeling requires tuning of many parameters to fit the neuron model to target electrophysiological data. Automated parameter optimization via evolutionary algorithms (EAs) is a common approach to accomplish this task, using error functions to quantify differences between model and target. We present a three-stage EA optimization protocol for tuning ion channel conductances and kinetics in a generic neuron model with minimal manual intervention. We use the technique of Latin hypercube sampling in a new way, to choose weights for error functions automatically so that each function influences the parameter search to a similar degree. This protocol requires no specialized physiological data collection and is applicable to commonly-collected current clamp data and either single- or multi-objective optimization. We applied the protocol to two representative pyramidal neurons from layer 3 of the prefrontal cortex of rhesus monkeys, in which action potential firing rates are significantly higher in aged compared to young animals. Using an idealized dendritic topology and models with either 4 or 8 ion channels (10 or 23 free parameters respectively), we produced populations of parameter combinations fitting the target datasets in less than 80 hours of optimization each. Passive parameter differences between young and aged models were consistent with our prior results using simpler models and hand tuning. We analyzed parameter values among fits to a single neuron to facilitate refinement of the underlying model, and across fits to multiple neurons to show how our protocol will lead to predictions of parameter differences with aging in these neurons.

An Overview of Two-level Supersaturated Designs with Cyclic Structure

An overview is given of the link between the k-circulant method of construction of two-level supersaturated designs and construction methods based on cyclic incomplete block designs. It is shown that this link enables a simple formula for the Es2-efficiency of all such designs to be derived. Generators are given for Es2-optimal and near-optimal designs that extend the range of previously known designs or that have a smaller number of highly correlated column pairs.

Variation in the resilience of cloud forest vascular epiphytes to severe drought

Epiphytes are common in tropical montane cloud forests (TMCFs) and play many important ecological roles, but the degree to which these unique plants will be affected by changes in climate is unknown. We investigated the drought responses of three vascular epiphyte communities bracketing the cloud base during a severe, El Niño-impacted dry season. Epiphytes were instrumented with sap flow probes in each site. Leaf water potential and pressure-volume curve parameters were also measured before and during the drought. We monitored the canopy microclimate in each site to determine the drivers of sap velocity across the sites. All plants greatly reduced their water use during the drought, but recovery occurred more quickly for plants in the lower and drier sites. Plants in drier sites also exhibited the greatest shifts in the osmotic potential at full saturation and the turgor loss point. Although all individuals survived this intense drought, epiphytes in the cloud forest experienced the slowest recovery, suggesting that plants in the TMCF are particularly sensitive to severe drought. Although vapor pressure deficit was an important driver of sap velocity in the highest elevation site, other factors, such as the volumetric water content of the canopy soil, were more important at lower (and warmer) sites.

Vapor pressure deficit predicts epiphyte abundance across an elevational gradient in a tropical montane region

PREMISE OF THE STUDY Tropical Montane Cloud Forests (TMCFs) are important ecosystems to study and preserve because of their high biodiversity and critical roles in local and regional ecosystem processes. TMCFs may be particularly affected by changes in climate because of the narrow bands of microclimate they occupy and the vulnerability of TMCF species to projected increases in cloud base heights and drought. A comprehensive understanding of the structure and function of TMCFs is lacking and difficult to attain because of variation in topography within and across TMCF sites. This causes large differences in microclimate and forest structure at both large and small scales. METHODS In this study, we estimated the abundance of the entire epiphyte community in the canopy (bryophytes, herbaceous vascular plants, woody epiphytes, and canopy dead organic matter) in six sites. In each of the sites we installed a complete canopy weather station to link epiphyte abundance to a number of microclimatic parameters. KEY RESULTS We found significant differences in epiphyte abundance across the sites; epiphyte abundance increased with elevation and leaf wetness, but decreased as vapor pressure deficit (VPD) increased. Epiphyte abundance had the strongest relationship with VPD; there were differences in VPD that could not be explained by elevation alone. CONCLUSIONS By measuring this proxy of canopy VPD, TMCF researchers will better understand differences in microclimate and plant community composition across TMCF sites. Incorporating such information in comparative studies will allow for more meaningful comparisons across TMCFs and will further conservation and management efforts in this ecosystem.

Automatic fitness function selection for compartment model optimization

During normal aging, layer 3 pyramidal neurons of the rhesus monkey prefrontal cortex (PFC) exhibit significant morphological changes, as well as higher action potential firing rates in vitro [1]. Computational modeling of individual neurons can provide insight into the ionic mechanisms underlying the increased excitability, which are currently unknown. A unique database of electrophysiological recordings and morphologic reconstructions from the same neurons, gathered through whole-cell patch clamp recording, confocal microscopy and 3D digital tracing, constrains the models. Initial modeling of six young and six aged neurons demonstrated that morphological features alone do not account entirely for the electrophysiological changes with aging [2]. It is now necessary to explore the parameter space of passive cable properties and active membrane channel conductances and kinetics, to uncover parameter combinations that reproduce the firing patterns observed in neurons of each age group. Differential Evolution (DE) is an evolutionary optimization method capable of identifying a population of candidate models throughout parameter space that closely match empirically observed firing patterns. The quality of fit achieved by an optimization is reliant on the ‘fitness functions’ used to measure the accuracy of the model. Previous neuronal compartment modeling studies using parameter optimization have introduced multiple types of fitness measurement [3], but have not described a general method to determine weights for each type. Here we introduce a novel method for automatically establishing weights of minimally correlated fitness functions, and apply it to optimization of models of young and aged PFC neurons. First, a Latin hypercube design (< 1000 points) provides a space-filling sampling of parameter space; the candidate fitness functions are then evaluated at each of these points. Second, clusters of fitness functions that are highly correlated across the hypercube are pruned to leave one representative member. Third, a principal component analysis of the remaining fitness functions across the hypercube identifies a set of fitness functions representing most of the variability in the parameter space, which are selected for use in the optimization. Fourth, weights for each selected fitness function are calculated based on the combination of coefficients for principal components and variance explained by those components. Finally, DE is conducted on the Neuroscience Gateway [4] using this automatically constructed optimization protocol. We demonstrate the method with a compartment model comprising a simplified pyramidal neuron morphology and three ion channels, optimized to data from representative young and aged neurons. Compared to a manual approach involving iterative generation of fitness functions, our novel method produces better fitting models using a tenth of the computation time. Future work will extend the automatic protocol generation to prioritize which parameters to optimize, a critical step as more ion channels are added to the model to improve fitness. This method will be used to generate morphologically detailed models of 20+ young and aged PFC neurons, predicting which ionic mechanisms underlie age-related physiological changes.

Evaluating the effectiveness of urban trees to mitigate storm water runoff via transpiration and stemflow

Many cities in the Eastern United States are working to increase urban tree cover due to the hydrological services that trees provide, including the interception, storage and transpiration of water that would otherwise enter sewer systems. Despite the understanding that trees benefit urban ecosystems, there have been few studies that address the underlying physiology of different urban trees with regard to their capacity to take up water, particularly following rain events. We monitored the sap flow of nine species of trees in urban parks and linked sap flow to local microclimate. In addition, we measured throughfall, stemflow and crown architecture. Interspecific variation in sap flow was significant as were differences in time lags (i.e. difference in time between the increase of solar radiation versus sap flow) across species of large but not small trees. Interestingly the most important microclimatic drivers of sap flow were different in large versus small trees. Lastly, we found that trees with a large branch angle routed more rainwater to stemflow. In this study we found strong evidence that variation in urban tree physiology can impact important hydrological services that will influence the effectiveness of different trees as tools to manage stormwater runoff.

Optimizing Thin Film Tool Coatings Using a Finite Element Computer Simulator

ABSTRACT The application of thin, hard coatings is one of the most effective ways to protect an engineering component operating under heavy contact. We describe a computer experiment for improving the performance of a multilayer titanium nitride/titanium coating architecture using a computational simulator based on an axisymmetric finite element model. From 146 simulator runs made in two sets, the stresses in the coating material were evaluated and used to build a metamodel for optimizing the multilayer system. Complicating features of this engineering application were (1) a nonrectangular input region of coating designs and (2) opposing objectives to be minimized simultaneously.